Is there a correlation between a restaurants ratings and the income levels of a neighborhood ?

IBM Data Science Certificate Capstone Project

Introduction

Background

A few weeks ago, my girlfriend and her friends went out for a Saturday girls lunch at an upmarket and expensive restaurant in Johannesburg. The restaurant is set in an exculsive estate and boasts of very scenic views however my girlfriend and her friends did not enjoy it. The drinks were over-priced, the meal portions were little, the service was terrible and the food was mediocre at best. The ladies had to pass by a local KFC to get some chicken to soothe their appetite.

This experience made me wonder. Do we assume that expensive restaurants will have better service than less expensive restaurants. Is the service better in restaurants in higher income areas than in lower income areas? Is there a correlation between income levels and restaurant ratings? This is an interesting question because conventional opinion would say that restaurants in higher income areas have better service which leads to better ratings because the restaurant’s patrons will demand better service and/or the restaurant will offer better service when in a high income neighborhood because they can charge more. But can the data science prove that or is this biased thinking?

My study will look at the data and see whether we can confirm that restaurant service does differ depending on the income levels of the neighborhood. If the study shows there is a link between poor Foursquare ratings and income levels, this would be of major interest to business owners, to restaurant patrons and to a city's management team. For business owners running a restaurant, they might spot an opportunity to gain customers by improving their customer service especially in low income areas. Patrons living in low income neighborhoods would have the ammunition to demand better customer service in the restaurants they frequent. City hall officials might also see this as a form of income segregation and work harder to ensure restaurants in low income areas have the same amenities and service levels as those in middle or high income areas.

The city I have chosen for this study is Toronto, Canada. It’s a relatively wealthy city with a population of approximately 6.5 million people (2016 Canada census). It is also one of the most diverse, multicultural and multiracial cities in the world with 51% of the population made up of visible minorities according to the 2016 Canada census. The big and varied population has resulted in a large number of restaurants and fast food places offering all types of cuisine from Indian to Chinese to South Korean to Ethiopian. This is good for our study as it means we can get a diverse number of restaurants to sample.

Data acquisition and cleaning

Data Sources

We will use the following datasets to work on this study

A Toronto neighborhood dataset showing average income per neighborhood in Toronto. We have sourced this dataset from the Open Data website for the City of Toronto, http://map.toronto.ca/wellbeing A Toronto neighborhood restaurant dataset with average restaurant ratings. We will source this data from Yelp by collecting the ratings of restaurants in each Toronto neighborhood using the Toronto dataset above and the Yelp API. We initially wanted to use Foursquare however the Foursquare API has very little api calls when compared to Yelp. The Foursquare API allows 500 api calls per day for free while the Yelp api allows 5000 api calls per day for free. On top of that Yelp was built for reviews and ratings and has a much larger restaurant reviews database then Foursquare therefore it made more sense to use Yelp.

#librarys for data analsysis
import numpy as np
import pandas as pd

# library to handle requests
import requests

import types
from botocore.client import Config
import ibm_boto3


print('Libraries imported.')

#Import the Toronto neighborhood dataset csv from the http://map.toronto.ca/wellbeing

def __iter__(self): return 0

# @hidden_cell
# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.
# You might want to remove those credentials before you share your notebook.
client_ca0db7a662d240daae1b2c21c81d43e2 = ibm_boto3.client(service_name='s3',
    ibm_api_key_id='v2qixxJ3bd-6jl56gtgnRXlTl_xxTGlrCMb_7d28gY-n',
    ibm_auth_endpoint="https://iam.eu-gb.bluemix.net/oidc/token",
    config=Config(signature_version='oauth'),
    endpoint_url='https://s3.eu-geo.objectstorage.service.networklayer.com')

body = client_ca0db7a662d240daae1b2c21c81d43e2.get_object(Bucket='capstone-donotdelete-pr-o9xw13k7o8elci',Key='wellbeing_toronto-1.csv')['Body']
# add missing __iter__ method, so pandas accepts body as file-like object
if not hasattr(body, "__iter__"): body.__iter__ = types.MethodType( __iter__, body )

df = pd.read_csv(body)
df.head()

	Neighbourhood	Neighbourhood Id	Combined Indicators	Total Population	Total Area	After-Tax Household Income
0	West Humber-Clairville	1.0	NaN	33312.0	30.09	59703.0
1	Mount Olive-Silverstone-Jamestown	2.0	NaN	32954.0	4.60	46986.0
2	Thistletown-Beaumond Heights	3.0	NaN	10360.0	3.40	57522.0
3	Rexdale-Kipling	4.0	NaN	10529.0	2.50	51194.0
4	Elms-Old Rexdale	5.0	NaN	9456.0	2.90	49425.0

# drop unnecessary columns, rename some columns
df = df.drop(['Neighbourhood Id', 'Combined Indicators'], 1)
df = df.rename(columns = {"After-Tax Household Income": "Income", "Total Population": "Population", "Total Area": "Area"}) #rename to income to make it 
df.head()

	Neighbourhood	Population	Area	Income
0	West Humber-Clairville	33312.0	30.09	59703.0
1	Mount Olive-Silverstone-Jamestown	32954.0	4.60	46986.0
2	Thistletown-Beaumond Heights	10360.0	3.40	57522.0
3	Rexdale-Kipling	10529.0	2.50	51194.0
4	Elms-Old Rexdale	9456.0	2.90	49425.0

#first thing we need to do is get the latitude and longitude of the neighborhoods
# add the latitude/longitude columns
df['Latitude'] = None
df['Longitude'] = None
df.head()

	Neighbourhood	Population	Area	Income	Latitude	Longitude
0	West Humber-Clairville	33312.0	30.09	59703.0	None	None
1	Mount Olive-Silverstone-Jamestown	32954.0	4.60	46986.0	None	None
2	Thistletown-Beaumond Heights	10360.0	3.40	57522.0	None	None
3	Rexdale-Kipling	10529.0	2.50	51194.0	None	None
4	Elms-Old Rexdale	9456.0	2.90	49425.0	None	None

I used the Google Maps API to get the latitude and longitude of the Toronto neighborhoods because it gave the most accurate results and didn't time out

# key in the google maps api key and import a time function.
# Using the time function to add a small delay when querying the maps api so that it does not exceed the rate limit
api_key = 'AIzaSyBWnqcXLCv4Z9CurRzBqsMhFQM1IQQ_5Q8'
from time import sleep

limit = 0
latitude = []
longitude = []
for index, neighborhood in df.iterrows():
    address = neighborhood['Neighbourhood'] + ', Canada'
    geocode_url = "https://maps.googleapis.com/maps/api/geocode/json?address={}".format(address)
    geocode_url = geocode_url + "&key={}".format(api_key)
    results = requests.get(geocode_url).json()
    if len(results['results']) != 0:
        answer = results['results'][0]
        latitude.append(answer.get('geometry').get('location').get('lat'))
        longitude.append(answer.get('geometry').get('location').get('lng')) 
    
    #added in a delay so as not to exceed the api rate of request
    sleep(0.1)


df['Latitude'] = pd.DataFrame(latitude);
df['Longitude'] = pd.DataFrame(longitude);           
print('Latitude/Longitude added')

Latitude/Longitude added

Query the Yelp api to get the restaurants in a neighborhood. We are limiting to 50 restaurants within a 2000 meter radius of the neighborhood

##define Yelp credentials
LIMIT = 50
RADIUS = 2000
CATEGORYID = 'restaurants' #restaurant category id
API_KEY = '6KclwBLE93GvS8bWdVA6OVtQKuLblZuEcw5Im_GWeNDm6E_lE1IvlJyEgUUG1KuXs3S2Lqokn72FBe323yEpeaqbRBafS0RqmnfGx56ycAwirakJ5QY3fOk8RaRKXXYx'

#import modules needed for connecting to Yelp
import http.client
import json
import urllib.parse

#header authorization using Yelp api key
headers = {
'Authorization': 'Bearer %s' % API_KEY,
}

#query the Yelp api to get restaurants in a neighborhood
venues_list=[]
for lat, long, hood in zip(df['Latitude'], df['Longitude'], df['Neighbourhood']):
   
    # create the API request URL
    url = '/v3/businesses/search?latitude={}&longitude={}&radius={}&limit={}&categories={}&locale={}'.format(
            lat,
            long, 
            RADIUS, 
            LIMIT,
            CATEGORYID,
            'en_CA')

    # make the GET request
    conn = http.client.HTTPSConnection("api.yelp.com")
    conn.request("GET", url, headers=headers)

    res = conn.getresponse()
    data = res.read()
    results = json.loads(data.decode("utf-8"))
    
    venues_list.append([hood, results])
               
print(venues_list)

[['West Humber-Clairville', {'businesses': [{'id': 'MrwnT4FCdcLd_GmQPGJMyg', 'alias': 'samosa-and-sweet-factory-etobicoke', 'name': 'Samosa and Sweet Factory', 'image_url': 'https://s3-media1.fl.yelpcdn.com/bphoto/f8Ye8yv3pL3Y0Yw1r56B9Q/o.jpg', 'is_closed': False, 'url': 'https://www.yelp.ca/biz/samosa-and-sweet-factory-etobicoke?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 74, 'categories': [{'alias': 'indpak', 'title': 'Indian'}, {'alias': 'desserts', 'title': 'Desserts'}], 'rating': 4.0, 'coordinates': {'latitude': 43.7465, 'longitude': -79.61537}, 'transactions': [], 'price': '$', 'location': {'address1': '1850 Albion Road', 'address2': '', 'address3': '', 'city': 'Etobicoke', 'zip_code': 'M9W 6J9', 'country': 'CA', 'state': 'ON', 'display_address': ['1850 Albion Road', 'Etobicoke, ON M9W 6J9']}, 'phone': '+14162131165', 'display_phone': '(416) 213-1165', 'distance': 1439.2259401008664}, {'id': 'vwVoksbTKehDP8ZGUQaVfQ', 'alias': 'sweet-mahal-toronto', 'name': 'Sweet Mahal', 'image_url': 'https://s3-media1.fl.yelpcdn.com/bphoto/7sMM0e1kL6eWpNydUcFF7A/o.jpg', 'is_closed': False, 'url': 'https://www.yelp.ca/biz/sweet-mahal-toronto?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 34, 'categories': [{'alias': 'indpak', 'title': 'Indian'}], 'rating': 3.5, 'coordinates': {'latitude': 43.72063, 'longitude': -79.61405}, 'transactions': [], 'price': '$$', 'location': {'address1': '680 Rexdale Boulevard', 'address2': 'Unit 9', 'address3': '', 'city': 'Toronto', 'zip_code': 'M9W 0B5', 'country': 'CA', 'state': 'ON', 'display_address': ['680 Rexdale Boulevard', 'Unit 9', 'Toronto, ON M9W 0B5']}, 'phone': '+14166799191', 'display_phone': '(416) 679-9191', 'distance': 1888.650898837253}, {'id': 'w6O_5Tc9LdKxVcORZuJO2w', 'alias': 'exotic-indian-cuisine-etobicoke', 'name': 'Exotic Indian Cuisine', 'image_url': 'https://s3-media2.fl.yelpcdn.com/bphoto/ND0uXzSvRpolDS0ImDmOdQ/o.jpg', 'is_closed': False, 'url': 'https://www.yelp.ca/biz/exotic-indian-cuisine-etobicoke?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 7, 'categories': [{'alias': 'mideastern', 'title': 'Middle Eastern'}, {'alias': 'indpak', 'title': 'Indian'}, {'alias': 'pakistani', 'title': 'Pakistani'}], 'rating': 4.5, 'coordinates': {'latitude': 43.7465, 'longitude': -79.61537}, 'transactions': [], 'price': '$$', 'location': {'address1': '1850 Albion Road', 'address2': '', 'address3': '', 'city': 'Etobicoke', 'zip_code': 'M9W 6J9', 'country': 'CA', 'state': 'ON', 'display_address': ['1850 Albion Road', 'Etobicoke, ON M9W 6J9']}, 'phone': '+14167984449', 'display_phone': '(416) 798-4449', 'distance': 1439.2259401008664}, {'id': '5JEbkemJfFUAw-xqNLkjxQ', 'alias': 'mr-jerk-mississauga', 'name': 'Mr Jerk', 'image_url': 'https://s3-media1.fl.yelpcdn.com/bphoto/BRTue92EYW-ib9Qi-YybJg/o.jpg', 'is_closed': False, 'url': 'https://www.yelp.ca/biz/mr-jerk-mississauga?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 30, 'categories': [{'alias': 'caribbean', 'title': 'Caribbean'}], 'rating': 3.5, 'coordinates': {'latitude': 43.71305, 'longitude': -79.6333}, 'transactions': [], 'price': '$', 'location': {'address1': '3417 Derry Rd E', 'address2': '', 'address3': '', 'city': 'Mississauga', 'zip_code': 'L4T 1A8', 'country': 'CA', 'state': 'ON', 'display_address': ['3417 Derry Rd E', 'Mississauga, ON L4T 1A8']}, 'phone': '+19056735627', 'display_phone': '(905) 673-5627', 'distance': 2595.6733603206794}, {'id': 'kIjmPE4FxiNT843Se1CSTQ', 'alias': 'harveys-mississauga-11', 'name': "Harvey's", 'image_url': 'https://s3-media1.fl.yelpcdn.com/bphoto/WcGA9eosIUejgnOxyZPALg/o.jpg', 'is_closed': False, 'url': 'https://www.yelp.ca/biz/harveys-mississauga-11?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 7, 'categories': [{'alias': 'burgers', 'title': 'Burgers'}, {'alias': 'hotdogs', 'title': 'Fast Food'}, {'alias': 'tradamerican', 'title': 'American (Traditional)'}], 'rating': 4.5, 'coordinates': {'latitude': 43.7160215, 'longitude': -79.6269075}, 'transactions': [], 'location': {'address1': '6995 Rexwood Rd', 'address2': None, 'address3': None, 'city': 'Mississauga', 'zip_code': 'L4T 1N1', 'country': 'CA', 'state': 'ON', 'display_address': ['6995 Rexwood Rd', 'Mississauga, ON L4T 1N1']}, 'phone': '+19056127336', 'display_phone': '(905) 612-7336', 'distance': 2190.296465966655}, {'id': 'SAukdfl2MktCHZCvRJC4Dg', 'alias': 'shayona-etobicoke', 'name': 'Shayona', 'image_url': 'https://s3-media2.fl.yelpcdn.com/bphoto/u-fKxaCvq6IOfswUVpGzIw/o.jpg', 'is_closed': False, 'url': 'https://www.yelp.ca/biz/shayona-etobicoke?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 2, 'categories': [{'alias': 'indpak', 'title': 'Indian'}], 'rating': 5.0, 'coordinates': {'latitude': 43.73799, 'longitude': -79.62289}, 'transactions': [], 'location': {'address1': '46 Claireville Drive', 'address2': '', 'address3': '', 'city': 'Etobicoke', 'zip_code': 'M9W 5T9', 'country': 'CA', 'state': 'ON', 'display_address': ['46 Claireville Drive', 'Etobicoke, ON M9W 5T9']}, 'phone': '+14169875097', 'display_phone': '(416) 987-5097', 'distance': 355.1818833452553}, {'id': 'xYe-LX3lSKlNS3Wp29_YyA', 'alias': 'china-palace-mississauga', 'name': 'China Palace', 'image_url': 'https://s3-media1.fl.yelpcdn.com/bphoto/D4EceGz7yi44CS-O-XLNyw/o.jpg', 'is_closed': False, 'url': 'https://www.yelp.ca/biz/china-palace-mississauga?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 25, 'categories': [{'alias': 'chinese', 'title': 'Chinese'}], 'rating': 3.0, 'coordinates': {'latitude': 43.71414, 'longitude': -79.62992}, 'transactions': [], 'price': '$$', 'location': {'address1': '3530 Derry Rd E', 'address2': '', 'address3': '', 'city': 'Mississauga', 'zip_code': 'L4T 4E3', 'country': 'CA', 'state': 'ON', 'display_address': ['3530 Derry Rd E', 'Mississauga, ON L4T 4E3']}, 'phone': '+19054051118', 'display_phone': '(905) 405-1118', 'distance': 2413.522202534068}, {'id': 'YOFvIxU3xQH7jmqHcUjofQ', 'alias': 'the-kebob-mississauga', 'name': 'The Kebob', 'image_url': 'https://s3-media3.fl.yelpcdn.com/bphoto/Tdn1tUdhJW6SPYYlwjzrnw/o.jpg', 'is_closed': False, 'url': 'https://www.yelp.ca/biz/the-kebob-mississauga?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 10, 'categories': [{'alias': 'halal', 'title': 'Halal'}, {'alias': 'pakistani', 'title': 'Pakistani'}], 'rating': 4.0, 'coordinates': {'latitude': 43.71305, 'longitude': -79.6333}, 'transactions': [], 'price': '$$', 'location': {'address1': '3417 Derry Road E', 'address2': '', 'address3': '', 'city': 'Mississauga', 'zip_code': 'L4T 1A8', 'country': 'CA', 'state': 'ON', 'display_address': ['3417 Derry Road E', 'Mississauga, ON L4T 1A8']}, 'phone': '+19056782324', 'display_phone': '(905) 678-2324', 'distance': 2595.6733603206794}, {'id': 'KWKZvqNtsnPQltn6ZxOjDw', 'alias': 'al-meezans-spicy-grill-toronto', 'name': 'Al-Meezans Spicy Grill', 'image_url': 'https://s3-media2.fl.yelpcdn.com/bphoto/luiXQG72dp7k2ylrkRuoEg/o.jpg', 'is_closed': False, 'url': 'https://www.yelp.ca/biz/al-meezans-spicy-grill-toronto?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 15, 'categories': [{'alias': 'mideastern', 'title': 'Middle Eastern'}, {'alias': 'afghani', 'title': 'Afghan'}], 'rating': 3.0, 'coordinates': {'latitude': 43.72075, 'longitude': -79.6132499}, 'transactions': [], 'price': '$', 'location': {'address1': '670 Rexdale Boulevard', 'address2': 'Unit 4', 'address3': '', 'city': 'Toronto', 'zip_code': 'M9W 0B5', 'country': 'CA', 'state': 'ON', 'display_address': ['670 Rexdale Boulevard', 'Unit 4', 'Toronto, ON M9W 0B5']}, 'phone': '+14166757775', 'display_phone': '(416) 675-7775', 'distance': 1951.997107536687}, {'id': 'd2VIjNbJpJterw--jU-hUQ', 'alias': 'da-house-of-jerk-etobicoke', 'name': 'Da House Of Jerk', 'image_url': 'https://s3-media4.fl.yelpcdn.com/bphoto/R1xHMgxQMcnBczdWD84KeQ/o.jpg', 'is_closed': False, 'url': 'https://www.yelp.ca/biz/da-house-of-jerk-etobicoke?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 18, 'categories': [{'alias': 'caribbean', 'title': 'Caribbean'}], 'rating': 2.5, 'coordinates': {'latitude': 43.72063, 'longitude': -79.61405}, 'transactions': [], 'price': '$$$', 'location': {'address1': '680 Rexdale Blvd', 'address2': '', 'address3': '', 'city': 'Etobicoke', 'zip_code': 'M9W 7J6', 'country': 'CA', 'state': 'ON', 'display_address': ['680 Rexdale Blvd', 'Etobicoke, ON M9W 7J6']}, 'phone': '+14166790755', 'display_phone': '(416) 679-0755', 'distance': 1888.841701450475}, {'id': 'HHG2nvtzEi0nNYH1b1es2A', 'alias': 'canosa-bakery-etobicoke', 'name': 'Canosa Bakery', 'image_url': 'https://s3-media2.fl.yelpcdn.com/bphoto/IFSm-5SUj64XGK64E4cyFg/o.jpg', 'is_closed': False, 'url': 'https://www.yelp.ca/biz/canosa-bakery-etobicoke?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 3, 'categories': [{'alias': 'bakeries', 'title': 'Bakeries'}, {'alias': 'italian', 'title': 'Italian'}], 'rating': 4.5, 'coordinates': {'latitude': 43.74991, 'longitude': -79.60798}, 'transactions': [], 'location': {'address1': '21 Royalcrest Road', 'address2': '', 'address3': '', 'city': 'Etobicoke', 'zip_code': 'M9V 2L6', 'country': 'CA', 'state': 'ON', 'display_address': ['21 Royalcrest Road', 'Etobicoke, ON M9V 2L6']}, 'phone': '+14167434579', 'display_phone': '(416) 743-4579', 'distance': 2129.166975972385}, {'id': 'Hy7-hTDuobP0FNxORRtSGA', 'alias': 'smokes-poutinerie-toronto-9', 'name': "Smoke's Poutinerie", 'image_url': '', 'is_closed': False, 'url': 'https://www.yelp.ca/biz/smokes-poutinerie-toronto-9?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 2, 'categories': [{'alias': 'newcanadian', 'title': 'Canadian (New)'}], 'rating': 4.5, 'coordinates': {'latitude': 43.7279194585731, 'longitude': -79.6090384572744}, 'transactions': [], 'location': {'address1': '205 Humber College Boulevard', 'address2': '', 'address3': '', 'city': 'Toronto', 'zip_code': 'M9W', 'country': 'CA', 'state': 'ON', 'display_address': ['205 Humber College Boulevard', 'Toronto, ON M9W']}, 'phone': '+14166755000', 'display_phone': '(416) 675-5000', 'distance': 1600.5973164511893}, {'id': 'RnK2drcN6UWtLODOsY7AVg', 'alias': 'the-humber-room-toronto', 'name': 'The Humber Room', 'image_url': 'https://s3-media1.fl.yelpcdn.com/bphoto/WkwLvML5EAzkmYRnx1C-dg/o.jpg', 'is_closed': False, 'url': 'https://www.yelp.ca/biz/the-humber-room-toronto?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 1, 'categories': [{'alias': 'tradamerican', 'title': 'American (Traditional)'}, {'alias': 'desserts', 'title': 'Desserts'}, {'alias': 'salad', 'title': 'Salad'}], 'rating': 5.0, 'coordinates': {'latitude': 43.7289241883024, 'longitude': -79.6090025827289}, 'transactions': [], 'location': {'address1': '205 Humber College Blvd', 'address2': '', 'address3': 'Humber College', 'city': 'Toronto', 'zip_code': 'M9W 5L7', 'country': 'CA', 'state': 'ON', 'display_address': ['205 Humber College Blvd', 'Humber College', 'Toronto, ON M9W 5L7']}, 'phone': '+14166755022', 'display_phone': '(416) 675-5022', 'distance': 1545.9965561764548}, {'id': 'cTXVWy-OYfDn8YAVu2vutA', 'alias': 'mcdonalds-malton', 'name': "McDonald's", 'image_url': 'https://s3-media4.fl.yelpcdn.com/bphoto/xV-WTH4If0YlaCLDoS8Miw/o.jpg', 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'https://www.yelp.ca/biz/food-kulture-bistro-toronto?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 1, 'categories': [{'alias': 'breakfast_brunch', 'title': 'Breakfast & Brunch'}, {'alias': 'soup', 'title': 'Soup'}, {'alias': 'poutineries', 'title': 'Poutineries'}], 'rating': 2.0, 'coordinates': {'latitude': 43.77162, 'longitude': -79.20918}, 'transactions': [], 'location': {'address1': '136 Orton Park Road', 'address2': '', 'address3': '', 'city': 'Toronto', 'zip_code': 'M1G 3H1', 'country': 'CA', 'state': 'ON', 'display_address': ['136 Orton Park Road', 'Toronto, ON M1G 3H1']}, 'phone': '+14162891162', 'display_phone': '(416) 289-1162', 'distance': 2484.3538509279774}, {'id': 'Am_E8nodPvtQlrRV5ImvUg', 'alias': 'taste-n-flavour-toronto', 'name': 'Taste N Flavour', 'image_url': 'https://s3-media4.fl.yelpcdn.com/bphoto/U0wf5eDmEB2D9JHELeKgWQ/o.jpg', 'is_closed': False, 'url': 'https://www.yelp.ca/biz/taste-n-flavour-toronto?adjust_creative=140atsfCIWJM5mYVogRZLw&utm_campaign=yelp_api_v3&utm_medium=api_v3_business_search&utm_source=140atsfCIWJM5mYVogRZLw', 'review_count': 1, 'categories': [{'alias': 'chicken_wings', 'title': 'Chicken Wings'}, {'alias': 'tradamerican', 'title': 'American (Traditional)'}, {'alias': 'pubs', 'title': 'Pubs'}], 'rating': 1.0, 'coordinates': {'latitude': 43.77459, 'longitude': -79.17717}, 'transactions': [], 'location': {'address1': '4637 Kingston Road', 'address2': 'Unit 1', 'address3': '', 'city': 'Toronto', 'zip_code': 'M1E 2P8', 'country': 'CA', 'state': 'ON', 'display_address': ['4637 Kingston Road', 'Unit 1', 'Toronto, ON M1E 2P8']}, 'phone': '+14167240970', 'display_phone': '(416) 724-0970', 'distance': 2729.3803200082066}], 'total': 50, 'region': {'center': {'longitude': -79.19277699999999, 'latitude': 43.752743}}}]]

#loop through venue list, get the parameters we want and put them in a new array
hood_ratings = []
hood_review_count = [] #we want to get the total review count per neighborhood 
total_review_count = 0
for hood, venues in venues_list:
    for venue in venues['businesses']:
        hood_ratings.append([hood, venue['review_count'], venue['rating'], venue['categories'][0]['title']])
        total_review_count += venue['review_count']
       
    # print('%s - %s ' %(hood, total_review_count))
    hood_ratings.append([hood, total_review_count])
    total_review_count = 0
    
hood_ratings

[['West Humber-Clairville', 74, 4.0, 'Indian'],
 ['West Humber-Clairville', 34, 3.5, 'Indian'],
 ['West Humber-Clairville', 7, 4.5, 'Middle Eastern'],
 ['West Humber-Clairville', 30, 3.5, 'Caribbean'],
 ['West Humber-Clairville', 7, 4.5, 'Burgers'],
 ['West Humber-Clairville', 2, 5.0, 'Indian'],
 ['West Humber-Clairville', 25, 3.0, 'Chinese'],
 ['West Humber-Clairville', 10, 4.0, 'Halal'],
 ['West Humber-Clairville', 15, 3.0, 'Middle Eastern'],
 ['West Humber-Clairville', 18, 2.5, 'Caribbean'],
 ['West Humber-Clairville', 3, 4.5, 'Bakeries'],
 ['West Humber-Clairville', 2, 4.5, 'Canadian (New)'],
 ['West Humber-Clairville', 1, 5.0, 'American (Traditional)'],
 ['West Humber-Clairville', 8, 3.0, 'Fast Food'],
 ['West Humber-Clairville', 1, 5.0, 'Food Court'],
 ['West Humber-Clairville', 1, 5.0, 'Burgers'],
 ['West Humber-Clairville', 4, 3.5, 'Chicken Wings'],
 ['West Humber-Clairville', 2, 3.0, 'Pizza'],
 ['West Humber-Clairville', 2, 3.5, 'Mediterranean'],
 ['West Humber-Clairville', 7, 2.0, 'Pizza'],
 ['West Humber-Clairville', 4, 2.5, 'Chicken Wings'],
 ['West Humber-Clairville', 2, 3.5, 'Caribbean'],
 ['West Humber-Clairville', 2, 5.0, 'Italian'],
 ['West Humber-Clairville', 4, 2.0, 'Pizza'],
 ['West Humber-Clairville', 1, 5.0, 'Indian'],
 ['West Humber-Clairville', 7, 2.0, 'Thai'],
 ['West Humber-Clairville', 1, 1.0, 'African'],
 ['West Humber-Clairville', 1, 1.0, 'Indian'],
 ['West Humber-Clairville', 1, 3.0, 'Sandwiches'],
 ['West Humber-Clairville', 2, 1.0, 'Pizza'],
 ['West Humber-Clairville', 278],
 ['Mount Olive-Silverstone-Jamestown', 45, 4.0, 'Indian'],
 ['Mount Olive-Silverstone-Jamestown', 31, 3.5, 'Pakistani'],
 ['Mount Olive-Silverstone-Jamestown', 38, 4.0, 'Hakka'],
 ['Mount Olive-Silverstone-Jamestown', 45, 3.0, 'Halal'],
 ['Mount Olive-Silverstone-Jamestown', 11, 3.0, 'Donairs'],
 ['Mount Olive-Silverstone-Jamestown', 16, 3.5, 'Middle Eastern'],
 ['Mount Olive-Silverstone-Jamestown', 2, 5.0, 'Chinese'],
 ['Mount Olive-Silverstone-Jamestown', 17, 3.0, 'Indian'],
 ['Mount Olive-Silverstone-Jamestown', 12, 4.5, 'Italian'],
 ['Mount Olive-Silverstone-Jamestown', 26, 3.0, 'Buffets'],
 ['Mount Olive-Silverstone-Jamestown', 4, 4.5, 'Caribbean'],
 ['Mount Olive-Silverstone-Jamestown', 3, 5.0, 'Sushi Bars'],
 ['Mount Olive-Silverstone-Jamestown', 8, 3.0, 'Indian'],
 ['Mount Olive-Silverstone-Jamestown', 63, 2.5, 'Mexican'],
 ['Mount Olive-Silverstone-Jamestown', 8, 3.0, 'Indian'],
 ['Mount Olive-Silverstone-Jamestown', 2, 5.0, 'Mexican'],
 ['Mount Olive-Silverstone-Jamestown', 10, 4.0, 'Japanese'],
 ['Mount Olive-Silverstone-Jamestown', 3, 4.5, 'Hakka'],
 ['Mount Olive-Silverstone-Jamestown', 8, 3.5, 'Chinese'],
 ['Mount Olive-Silverstone-Jamestown', 2, 4.5, 'Indian'],
 ['Mount Olive-Silverstone-Jamestown', 3, 5.0, 'Caribbean'],
 ['Mount Olive-Silverstone-Jamestown', 7, 3.0, 'Greek'],
 ['Mount Olive-Silverstone-Jamestown', 2, 3.5, 'Fast Food'],
 ['Mount Olive-Silverstone-Jamestown', 1, 5.0, 'Pizza'],
 ['Mount Olive-Silverstone-Jamestown', 2, 5.0, 'Desserts'],
 ['Mount Olive-Silverstone-Jamestown', 5, 3.5, 'Caribbean'],
 ['Mount Olive-Silverstone-Jamestown', 5, 2.5, 'Burgers'],
 ['Mount Olive-Silverstone-Jamestown', 6, 3.0, 'Hot Dogs'],
 ['Mount Olive-Silverstone-Jamestown', 3, 3.5, 'Pizza'],
 ['Mount Olive-Silverstone-Jamestown', 1, 4.0, 'Pizza'],
 ['Mount Olive-Silverstone-Jamestown', 3, 4.0, 'Thai'],
 ['Mount Olive-Silverstone-Jamestown', 1, 5.0, 'Caterers'],
 ['Mount Olive-Silverstone-Jamestown', 4, 3.5, 'Burgers'],
 ['Mount Olive-Silverstone-Jamestown', 3, 4.0, 'Steakhouses'],
 ['Mount Olive-Silverstone-Jamestown', 3, 2.5, 'Fast Food'],
 ['Mount Olive-Silverstone-Jamestown', 3, 2.5, 'Chicken Wings'],
 ['Mount Olive-Silverstone-Jamestown', 5, 3.0, 'Pizza'],
 ['Mount Olive-Silverstone-Jamestown', 2, 4.0, 'Chinese'],
 ['Mount Olive-Silverstone-Jamestown', 2, 4.5, 'Caribbean'],
 ['Mount Olive-Silverstone-Jamestown', 3, 1.5, 'Pizza'],
 ['Mount Olive-Silverstone-Jamestown', 1, 5.0, 'Fast Food'],
 ['Mount Olive-Silverstone-Jamestown', 6, 2.5, 'Pizza'],
 ['Mount Olive-Silverstone-Jamestown', 1, 2.0, 'Caribbean'],
 ['Mount Olive-Silverstone-Jamestown', 2, 2.5, 'Pizza'],
 ['Mount Olive-Silverstone-Jamestown', 3, 3.0, 'Greek'],
 ['Mount Olive-Silverstone-Jamestown', 1, 4.0, 'Sandwiches'],
 ['Mount Olive-Silverstone-Jamestown', 1, 1.0, 'Asian Fusion'],
 ['Mount Olive-Silverstone-Jamestown', 3, 2.0, 'Indian'],
 ['Mount Olive-Silverstone-Jamestown', 1, 5.0, 'Lounges'],
 ['Mount Olive-Silverstone-Jamestown', 2, 2.0, 'Indian'],
 ['Mount Olive-Silverstone-Jamestown', 439],
 ['Thistletown-Beaumond Heights', 66, 4.0, 'Pakistani'],
 ['Thistletown-Beaumond Heights', 31, 3.5, 'Indian'],
 ['Thistletown-Beaumond Heights', 35, 3.5, 'Indian'],
 ['Thistletown-Beaumond Heights', 22, 4.0, 'Chinese'],
 ['Thistletown-Beaumond Heights', 20, 4.0, 'Caribbean'],
 ['Thistletown-Beaumond Heights', 11, 4.0, 'Indian'],
 ['Thistletown-Beaumond Heights', 33, 3.0, 'Indian'],
 ['Thistletown-Beaumond Heights', 12, 4.5, 'Italian'],
 ['Thistletown-Beaumond Heights', 6, 4.5, 'Caribbean'],
 ['Thistletown-Beaumond Heights', 10, 3.5, 'Chinese'],
 ['Thistletown-Beaumond Heights', 8, 5.0, 'Italian'],
 ['Thistletown-Beaumond Heights', 12, 3.5, 'Thai'],
 ['Thistletown-Beaumond Heights', 6, 4.0, 'Caribbean'],
 ['Thistletown-Beaumond Heights', 63, 2.5, 'Mexican'],
 ['Thistletown-Beaumond Heights', 17, 3.5, 'Breakfast & Brunch'],
 ['Thistletown-Beaumond Heights', 2, 5.0, 'Chinese'],
 ['Thistletown-Beaumond Heights', 3, 5.0, 'Caribbean'],
 ['Thistletown-Beaumond Heights', 2, 4.5, 'Pakistani'],
 ['Thistletown-Beaumond Heights', 4, 4.0, 'Pizza'],
 ['Thistletown-Beaumond Heights', 8, 3.0, 'Indian'],
 ['Thistletown-Beaumond Heights', 9, 2.5, 'Pizza'],
 ['Thistletown-Beaumond Heights', 5, 3.0, 'Bakeries'],
 ['Thistletown-Beaumond Heights', 3, 3.5, 'Fast Food'],
 ['Thistletown-Beaumond Heights', 6, 4.0, 'Lebanese'],
 ['Thistletown-Beaumond Heights', 5, 2.5, 'Pakistani'],
 ['Thistletown-Beaumond Heights', 2, 4.0, 'African'],
 ['Thistletown-Beaumond Heights', 4, 3.0, 'Halal'],
 ['Thistletown-Beaumond Heights', 2, 4.5, 'Colombian'],
 ['Thistletown-Beaumond Heights', 4, 2.5, 'Indian'],
 ['Thistletown-Beaumond Heights', 3, 2.5, 'Indian'],
 ['Thistletown-Beaumond Heights', 1, 5.0, 'Caterers'],
 ['Thistletown-Beaumond Heights', 4, 4.0, 'African'],
 ['Thistletown-Beaumond Heights', 2, 3.5, 'Fast Food'],
 ['Thistletown-Beaumond Heights', 1, 4.0, 'Indian'],
 ['Thistletown-Beaumond Heights', 2, 3.0, 'Cafes'],
 ['Thistletown-Beaumond Heights', 5, 2.5, 'Caribbean'],
 ['Thistletown-Beaumond Heights', 1, 5.0, 'Pizza'],
 ['Thistletown-Beaumond Heights', 2, 4.5, 'Venues & Event Spaces'],
 ['Thistletown-Beaumond Heights', 4, 3.5, 'Caribbean'],
 ['Thistletown-Beaumond Heights', 1, 4.0, 'Pakistani'],
 ['Thistletown-Beaumond Heights', 9, 3.0, 'Pizza'],
 ['Thistletown-Beaumond Heights', 1, 4.0, 'Pizza'],
 ['Thistletown-Beaumond Heights', 1, 5.0, 'Breakfast & Brunch'],
 ['Thistletown-Beaumond Heights', 3, 3.5, 'Pizza'],
 ['Thistletown-Beaumond Heights', 3, 2.5, 'Italian'],
 ['Thistletown-Beaumond Heights', 1, 4.0, 'Pizza'],
 ['Thistletown-Beaumond Heights', 2, 2.5, 'Pizza'],
 ['Thistletown-Beaumond Heights', 3, 3.5, 'Burgers'],
 ['Thistletown-Beaumond Heights', 3, 2.5, 'Chicken Wings'],
 ['Thistletown-Beaumond Heights', 1, 2.0, 'Caribbean'],
 ['Thistletown-Beaumond Heights', 464],
 ['Rexdale-Kipling', 66, 4.0, 'Pakistani'],
 ['Rexdale-Kipling', 37, 4.0, 'African'],
 ['Rexdale-Kipling', 47, 3.5, 'Indian'],
 ['Rexdale-Kipling', 20, 4.5, 'Mediterranean'],
 ['Rexdale-Kipling', 35, 4.5, 'Diners'],
 ['Rexdale-Kipling', 23, 4.0, 'Asian Fusion'],
 ['Rexdale-Kipling', 12, 4.5, 'Italian'],
 ['Rexdale-Kipling', 17, 3.5, 'Breakfast & Brunch'],
 ['Rexdale-Kipling', 25, 4.0, 'Seafood'],
 ['Rexdale-Kipling', 22, 4.0, 'Chinese'],
 ['Rexdale-Kipling', 17, 3.5, 'Filipino'],
 ['Rexdale-Kipling', 31, 3.5, 'Indian'],
 ['Rexdale-Kipling', 35, 3.5, 'Indian'],
 ['Rexdale-Kipling', 15, 4.0, 'Mediterranean'],
 ['Rexdale-Kipling', 20, 4.0, 'Caribbean'],
 ['Rexdale-Kipling', 14, 3.5, 'Afghan'],
 ['Rexdale-Kipling', 21, 3.5, 'Diners'],
 ['Rexdale-Kipling', 19, 3.5, 'African'],
 ['Rexdale-Kipling', 27, 3.0, 'Chinese'],
 ['Rexdale-Kipling', 6, 4.0, 'Afghan'],
 ['Rexdale-Kipling', 18, 3.5, 'Greek'],
 ['Rexdale-Kipling', 4, 5.0, 'Sandwiches'],
 ['Rexdale-Kipling', 11, 4.0, 'Indian'],
 ['Rexdale-Kipling', 33, 3.0, 'Indian'],
 ['Rexdale-Kipling', 7, 4.0, 'Breakfast & Brunch'],
 ['Rexdale-Kipling', 6, 4.0, 'Middle Eastern'],
 ['Rexdale-Kipling', 10, 3.5, 'Caribbean'],
 ['Rexdale-Kipling', 4, 3.5, 'Caribbean'],
 ['Rexdale-Kipling', 12, 3.5, 'Thai'],
 ['Rexdale-Kipling', 3, 3.5, 'Burgers'],
 ['Rexdale-Kipling', 10, 3.5, 'Chinese'],
 ['Rexdale-Kipling', 2, 5.0, 'Butcher'],
 ['Rexdale-Kipling', 4, 4.0, 'African'],
 ['Rexdale-Kipling', 4, 3.5, 'Afghan'],
 ['Rexdale-Kipling', 7, 2.5, 'Pizza'],
 ['Rexdale-Kipling', 5, 3.0, 'Mediterranean'],
 ['Rexdale-Kipling', 6, 4.0, 'Caribbean'],
 ['Rexdale-Kipling', 15, 2.5, 'Middle Eastern'],
 ['Rexdale-Kipling', 4, 4.0, 'Pizza'],
 ['Rexdale-Kipling', 9, 3.0, 'Pizza'],
 ['Rexdale-Kipling', 1, 5.0, 'Delis'],
 ['Rexdale-Kipling', 1, 4.0, 'Italian'],
 ['Rexdale-Kipling', 1, 5.0, 'Caterers'],
 ['Rexdale-Kipling', 9, 1.5, 'Fast Food'],
 ['Rexdale-Kipling', 2, 5.0, 'Chinese'],
 ['Rexdale-Kipling', 3, 3.5, 'Caribbean'],
 ['Rexdale-Kipling', 9, 2.5, 'Pizza'],
 ['Rexdale-Kipling', 2, 4.5, 'Pakistani'],
 ['Rexdale-Kipling', 3, 3.5, 'Fast Food'],
 ['Rexdale-Kipling', 2, 4.0, 'African'],
 ['Rexdale-Kipling', 716],
 ['Elms-Old Rexdale', 66, 4.0, 'Pakistani'],
 ['Elms-Old Rexdale', 47, 3.5, 'Indian'],
 ['Elms-Old Rexdale', 35, 4.5, 'Diners'],
 ['Elms-Old Rexdale', 25, 4.0, 'Seafood'],
 ['Elms-Old Rexdale', 22, 4.0, 'Chinese'],
 ['Elms-Old Rexdale', 14, 4.5, 'Italian'],
 ['Elms-Old Rexdale', 23, 4.0, 'Asian Fusion'],
 ['Elms-Old Rexdale', 31, 3.5, 'Indian'],
 ['Elms-Old Rexdale', 66, 3.0, 'Japanese'],
 ['Elms-Old Rexdale', 35, 3.5, 'Indian'],
 ['Elms-Old Rexdale', 20, 4.0, 'Caribbean'],
 ['Elms-Old Rexdale', 14, 3.5, 'Afghan'],
 ['Elms-Old Rexdale', 16, 4.0, 'Latin American'],
 ['Elms-Old Rexdale', 17, 3.5, 'Filipino'],
 ['Elms-Old Rexdale', 5, 4.5, 'Barbeque'],
 ['Elms-Old Rexdale', 19, 3.5, 'African'],
 ['Elms-Old Rexdale', 4, 5.0, 'Sandwiches'],
 ['Elms-Old Rexdale', 22, 3.5, 'Vietnamese'],
 ['Elms-Old Rexdale', 7, 4.5, 'Portuguese'],
 ['Elms-Old Rexdale', 11, 4.0, 'Indian'],
 ['Elms-Old Rexdale', 10, 3.5, 'Caribbean'],
 ['Elms-Old Rexdale', 33, 3.0, 'Indian'],
 ['Elms-Old Rexdale', 21, 3.5, 'Diners'],
 ['Elms-Old Rexdale', 4, 4.0, 'Hakka'],
 ['Elms-Old Rexdale', 7, 4.0, 'Breakfast & Brunch'],
 ['Elms-Old Rexdale', 12, 3.5, 'Thai'],
 ['Elms-Old Rexdale', 4, 4.0, 'African'],
 ['Elms-Old Rexdale', 10, 3.5, 'Chinese'],
 ['Elms-Old Rexdale', 5, 4.0, 'Caribbean'],
 ['Elms-Old Rexdale', 2, 5.0, 'Butcher'],
 ['Elms-Old Rexdale', 1, 5.0, 'American (Traditional)'],
 ['Elms-Old Rexdale', 4, 4.0, 'Pizza'],
 ['Elms-Old Rexdale', 4, 3.5, 'Afghan'],
 ['Elms-Old Rexdale', 4, 4.5, 'Pizza'],
 ['Elms-Old Rexdale', 9, 3.0, 'Pizza'],
 ['Elms-Old Rexdale', 14, 2.5, 'Italian'],
 ['Elms-Old Rexdale', 3, 3.5, 'Caribbean'],
 ['Elms-Old Rexdale', 5, 3.0, 'Mediterranean'],
 ['Elms-Old Rexdale', 15, 2.5, 'Middle Eastern'],
 ['Elms-Old Rexdale', 10, 2.5, 'African'],
 ['Elms-Old Rexdale', 2, 4.0, 'African'],
 ['Elms-Old Rexdale', 8, 2.5, 'Diners'],
 ['Elms-Old Rexdale', 9, 2.5, 'Pizza'],
 ['Elms-Old Rexdale', 25, 1.5, 'Fast Food'],
 ['Elms-Old Rexdale', 3, 3.5, 'Fast Food'],
 ['Elms-Old Rexdale', 1, 5.0, 'Delis'],
 ['Elms-Old Rexdale', 3, 3.0, 'Cafes'],
 ['Elms-Old Rexdale', 3, 3.0, 'Pakistani'],
 ['Elms-Old Rexdale', 5, 3.0, 'Bakeries'],
 ['Elms-Old Rexdale', 3, 2.5, 'Sandwiches'],
 ['Elms-Old Rexdale', 738],
 ['Kingsview Village-The Westway', 35, 4.5, 'Diners'],
 ['Kingsview Village-The Westway', 23, 4.0, 'African'],
 ['Kingsview Village-The Westway', 35, 3.5, 'Chinese'],
 ['Kingsview Village-The Westway', 37, 4.0, 'African'],
 ['Kingsview Village-The Westway', 47, 3.5, 'Indian'],
 ['Kingsview Village-The Westway', 18, 4.5, 'Seafood'],
 ['Kingsview Village-The Westway', 23, 4.0, 'Asian Fusion'],
 ['Kingsview Village-The Westway', 25, 4.0, 'Seafood'],
 ['Kingsview Village-The Westway', 17, 3.5, 'Filipino'],
 ['Kingsview Village-The Westway', 22, 3.5, 'Vietnamese'],
 ['Kingsview Village-The Westway', 14, 3.5, 'Afghan'],
 ['Kingsview Village-The Westway', 19, 3.5, 'African'],
 ['Kingsview Village-The Westway', 21, 3.5, 'Diners'],
 ['Kingsview Village-The Westway', 4, 5.0, 'Sandwiches'],
 ['Kingsview Village-The Westway', 27, 3.0, 'Chinese'],
 ['Kingsview Village-The Westway', 17, 3.5, 'Breakfast & Brunch'],
 ['Kingsview Village-The Westway', 7, 4.0, 'Breakfast & Brunch'],
 ['Kingsview Village-The Westway', 10, 3.5, 'Caribbean'],
 ['Kingsview Village-The Westway', 15, 2.5, 'Middle Eastern'],
 ['Kingsview Village-The Westway', 6, 4.0, 'Afghan'],
 ['Kingsview Village-The Westway', 16, 3.5, 'Asian Fusion'],
 ['Kingsview Village-The Westway', 7, 4.5, 'Portuguese'],
 ['Kingsview Village-The Westway', 5, 4.5, 'Barbeque'],
 ['Kingsview Village-The Westway', 2, 5.0, 'Butcher'],
 ['Kingsview Village-The Westway', 4, 3.5, 'Afghan'],
 ['Kingsview Village-The Westway', 5, 3.0, 'Mediterranean'],
 ['Kingsview Village-The Westway', 4, 4.0, 'Hakka'],
 ['Kingsview Village-The Westway', 8, 2.5, 'Diners'],
 ['Kingsview Village-The Westway', 25, 1.5, 'Fast Food'],
 ['Kingsview Village-The Westway', 1, 5.0, 'Delis'],
 ['Kingsview Village-The Westway', 7, 2.5, 'Pizza'],
 ['Kingsview Village-The Westway', 3, 3.5, 'Caribbean'],
 ['Kingsview Village-The Westway', 4, 4.0, 'African'],
 ['Kingsview Village-The Westway', 9, 3.0, 'Pizza'],
 ['Kingsview Village-The Westway', 3, 3.0, 'Pakistani'],
 ['Kingsview Village-The Westway', 8, 1.0, 'Fast Food'],
 ['Kingsview Village-The Westway', 3, 2.5, 'Sandwiches'],
 ['Kingsview Village-The Westway', 2, 3.5, 'Sandwiches'],
 ['Kingsview Village-The Westway', 3, 2.5, 'Fast Food'],
 ['Kingsview Village-The Westway', 2, 3.0, 'Lebanese'],
 ['Kingsview Village-The Westway', 1, 1.0, 'Pizza'],
 ['Kingsview Village-The Westway', 2, 1.5, 'Cafes'],
 ['Kingsview Village-The Westway', 3, 2.0, 'Fast Food'],
 ['Kingsview Village-The Westway', 1, 4.0, 'Mexican'],
 ['Kingsview Village-The Westway', 2, 1.0, 'Pizza'],
 ['Kingsview Village-The Westway', 3, 2.0, 'Pizza'],
 ['Kingsview Village-The Westway', 1, 5.0, 'Pizza'],
 ['Kingsview Village-The Westway', 1, 3.0, 'Fast Food'],
 ['Kingsview Village-The Westway', 1, 1.0, 'Middle Eastern'],
 ['Kingsview Village-The Westway', 1, 3.0, 'African'],
 ['Kingsview Village-The Westway', 559],
 ['Willowridge-Martingrove-Richview', 166, 4.0, 'Japanese'],
 ['Willowridge-Martingrove-Richview', 78, 3.5, 'Sports Bars'],
 ['Willowridge-Martingrove-Richview', 35, 4.5, 'Diners'],
 ['Willowridge-Martingrove-Richview', 67, 3.5, 'Steakhouses'],
 ['Willowridge-Martingrove-Richview', 18, 4.5, 'Seafood'],
 ['Willowridge-Martingrove-Richview', 91, 3.0, 'Buffets'],
 ['Willowridge-Martingrove-Richview', 39, 3.5, 'Asian Fusion'],
 ['Willowridge-Martingrove-Richview', 21, 4.5, 'Specialty Food'],
 ['Willowridge-Martingrove-Richview', 35, 3.5, 'Chinese'],
 ['Willowridge-Martingrove-Richview', 65, 2.5, 'Sushi Bars'],
 ['Willowridge-Martingrove-Richview', 45, 3.0, 'Canadian (New)'],
 ['Willowridge-Martingrove-Richview', 24, 3.5, 'Japanese'],
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 ['Kingsway South', 3, 4.5, 'Barbeque'],
 ['Kingsway South', 3, 5.0, 'Delis'],
 ['Kingsway South', 6, 3.5, 'Sandwiches'],
 ['Kingsway South', 1751],
 ['Stonegate-Queensway', 388, 4.5, 'Bakeries'],
 ['Stonegate-Queensway', 147, 4.5, 'Ice Cream & Frozen Yogurt'],
 ['Stonegate-Queensway', 57, 4.0, 'Thai'],
 ['Stonegate-Queensway', 315, 4.0, 'Breakfast & Brunch'],
 ['Stonegate-Queensway', 142, 4.0, 'Indian'],
 ['Stonegate-Queensway', 164, 4.0, 'Japanese'],
 ['Stonegate-Queensway', 56, 4.5, 'Polish'],
 ['Stonegate-Queensway', 124, 3.5, 'Italian'],
 ['Stonegate-Queensway', 116, 4.0, 'Thai'],
 ['Stonegate-Queensway', 47, 4.5, 'Pizza'],
 ['Stonegate-Queensway', 78, 4.0, 'Indian'],
 ['Stonegate-Queensway', 141, 3.5, 'Italian'],
 ['Stonegate-Queensway', 47, 4.0, 'Italian'],
 ['Stonegate-Queensway', 56, 4.0, 'Spanish'],
 ['Stonegate-Queensway', 33, 4.5, 'Japanese'],
 ['Stonegate-Queensway', 78, 3.5, 'Indian'],
 ['Stonegate-Queensway', 30, 4.0, 'Sushi Bars'],
 ['Stonegate-Queensway', 62, 4.5, 'Latin American'],
 ['Stonegate-Queensway', 23, 4.0, 'Cafes'],
 ['Stonegate-Queensway', 103, 3.5, 'Italian'],
 ['Stonegate-Queensway', 49, 3.0, 'Italian'],
 ['Stonegate-Queensway', 60, 4.0, 'Pubs'],
 ['Stonegate-Queensway', 59, 4.0, 'Indian'],
 ['Stonegate-Queensway', 73, 4.0, 'Falafel'],
 ['Stonegate-Queensway', 62, 4.0, 'Vegetarian'],
 ['Stonegate-Queensway', 40, 4.0, 'Indian'],
 ['Stonegate-Queensway', 56, 3.5, 'Coffee & Tea'],
 ['Stonegate-Queensway', 29, 3.5, 'Vietnamese'],
 ['Stonegate-Queensway', 50, 4.0, 'Breakfast & Brunch'],
 ['Stonegate-Queensway', 11, 4.0, 'Cocktail Bars'],
 ['Stonegate-Queensway', 71, 3.5, 'Italian'],
 ['Stonegate-Queensway', 30, 4.0, 'Modern European'],
 ['Stonegate-Queensway', 61, 3.5, 'Pizza'],
 ['Stonegate-Queensway', 49, 3.0, 'Modern European'],
 ['Stonegate-Queensway', 29, 3.5, 'Japanese'],
 ['Stonegate-Queensway', 45, 3.5, 'Bakeries'],
 ['Stonegate-Queensway', 20, 3.5, 'Breakfast & Brunch'],
 ['Stonegate-Queensway', 23, 3.0, 'Chinese'],
 ['Stonegate-Queensway', 48, 3.5, 'Sushi Bars'],
 ['Stonegate-Queensway', 69, 3.0, 'Burgers'],
 ['Stonegate-Queensway', 22, 3.5, 'Italian'],
 ['Stonegate-Queensway', 12, 4.0, 'American (Traditional)'],
 ['Stonegate-Queensway', 6, 4.5, 'Mediterranean'],
 ['Stonegate-Queensway', 6, 5.0, 'Cafes'],
 ['Stonegate-Queensway', 39, 3.5, 'Japanese'],
 ['Stonegate-Queensway', 10, 4.5, 'Canadian (New)'],
 ['Stonegate-Queensway', 50, 3.0, 'Thai'],
 ['Stonegate-Queensway', 27, 3.5, 'Sports Bars'],
 ['Stonegate-Queensway', 11, 3.5, 'Breakfast & Brunch'],
 ['Stonegate-Queensway', 29, 2.5, 'Coffee & Tea'],
 ['Stonegate-Queensway', 3353],
 ['Mimico', 388, 4.5, 'Bakeries'],
 ['Mimico', 164, 4.0, 'Japanese'],
 ['Mimico', 221, 4.5, 'Fast Food'],
 ['Mimico', 147, 4.5, 'Ice Cream & Frozen Yogurt'],
 ['Mimico', 116, 4.0, 'Thai'],
 ['Mimico', 148, 4.0, 'Sushi Bars'],
 ['Mimico', 78, 4.0, 'Indian'],
 ['Mimico', 142, 4.0, 'Indian'],
 ['Mimico', 56, 4.0, 'Spanish'],
 ['Mimico', 101, 4.0, 'Korean'],
 ['Mimico', 47, 4.5, 'Pizza'],
 ['Mimico', 56, 4.5, 'Polish'],
 ['Mimico', 82, 4.0, 'Pizza'],
 ['Mimico', 78, 3.5, 'Indian'],
 ['Mimico', 73, 4.0, 'Japanese'],
 ['Mimico', 57, 4.0, 'Thai'],
 ['Mimico', 82, 4.0, 'Thai'],
 ['Mimico', 103, 3.5, 'Italian'],
 ['Mimico', 47, 4.0, 'Italian'],
 ['Mimico', 28, 4.5, 'Mediterranean'],
 ['Mimico', 141, 3.5, 'Italian'],
 ['Mimico', 40, 4.0, 'Indian'],
 ['Mimico', 53, 4.0, 'Italian'],
 ['Mimico', 22, 4.0, 'Italian'],
 ['Mimico', 29, 3.5, 'Japanese'],
 ['Mimico', 33, 3.5, 'Italian'],
 ['Mimico', 19, 4.5, 'Breakfast & Brunch'],
 ['Mimico', 23, 4.0, 'Cafes'],
 ['Mimico', 24, 4.0, 'Fish & Chips'],
 ['Mimico', 23, 4.0, 'Coffee & Tea'],
 ['Mimico', 16, 4.0, 'Pizza'],
 ['Mimico', 17, 4.0, 'Cafes'],
 ['Mimico', 20, 3.5, 'Breakfast & Brunch'],
 ['Mimico', 30, 4.0, 'Sushi Bars'],
 ['Mimico', 40, 3.5, 'Portuguese'],
 ['Mimico', 21, 4.0, 'Sushi Bars'],
 ['Mimico', 29, 3.5, 'Vietnamese'],
 ['Mimico', 39, 3.5, 'Indian'],
 ['Mimico', 20, 3.5, 'Chinese'],
 ['Mimico', 37, 3.5, 'Sushi Bars'],
 ['Mimico', 18, 4.0, 'Seafood'],
 ['Mimico', 49, 3.0, 'Italian'],
 ['Mimico', 28, 3.5, 'Sushi Bars'],
 ['Mimico', 49, 3.0, 'Modern European'],
 ['Mimico', 20, 4.0, 'Caribbean'],
 ['Mimico', 21, 3.5, 'Italian'],
 ['Mimico', 5, 4.5, 'Cafes'],
 ['Mimico', 22, 3.5, 'Italian'],
 ['Mimico', 18, 3.0, 'American (Traditional)'],
 ['Mimico', 24, 3.5, 'Pizza'],
 ['Mimico', 3144],
 ['New Toronto', 388, 4.5, 'Bakeries'],
 ['New Toronto', 221, 4.5, 'Fast Food'],
 ['New Toronto', 28, 4.5, 'Mediterranean'],
 ['New Toronto', 164, 4.0, 'Japanese'],
 ['New Toronto', 53, 4.0, 'Italian'],
 ['New Toronto', 148, 4.0, 'Sushi Bars'],
 ['New Toronto', 116, 4.0, 'Thai'],
 ['New Toronto', 19, 4.5, 'Breakfast & Brunch'],
 ['New Toronto', 78, 4.0, 'Indian'],
 ['New Toronto', 142, 4.0, 'Indian'],
 ['New Toronto', 101, 4.0, 'Korean'],
 ['New Toronto', 24, 4.0, 'Fish & Chips'],
 ['New Toronto', 21, 4.0, 'Sushi Bars'],
 ['New Toronto', 17, 4.0, 'Cafes'],
 ['New Toronto', 23, 4.0, 'Coffee & Tea'],
 ['New Toronto', 56, 4.0, 'Spanish'],
 ['New Toronto', 39, 3.5, 'Indian'],
 ['New Toronto', 82, 4.0, 'Pizza'],
 ['New Toronto', 99, 4.0, 'Tex-Mex'],
 ['New Toronto', 20, 4.0, 'Caribbean'],
 ['New Toronto', 24, 3.5, 'Pizza'],
 ['New Toronto', 16, 4.5, 'Cafes'],
 ['New Toronto', 44, 3.5, 'Breakfast & Brunch'],
 ['New Toronto', 103, 3.5, 'Italian'],
 ['New Toronto', 12, 4.5, 'Fast Food'],
 ['New Toronto', 18, 4.0, 'Fish & Chips'],
 ['New Toronto', 40, 4.0, 'Indian'],
 ['New Toronto', 9, 4.5, 'Caribbean'],
 ['New Toronto', 170, 3.0, 'Sushi Bars'],
 ['New Toronto', 71, 3.5, 'Chinese'],
 ['New Toronto', 13, 3.5, 'Fish & Chips'],
 ['New Toronto', 71, 3.5, 'Canadian (New)'],
 ['New Toronto', 17, 3.5, 'Japanese'],
 ['New Toronto', 16, 4.0, 'Pizza'],
 ['New Toronto', 19, 3.5, 'Cafes'],
 ['New Toronto', 18, 3.5, 'Middle Eastern'],
 ['New Toronto', 53, 3.5, 'Lebanese'],
 ['New Toronto', 10, 3.5, 'Chicken Wings'],
 ['New Toronto', 40, 3.5, 'Portuguese'],
 ['New Toronto', 29, 3.5, 'Japanese'],
 ['New Toronto', 76, 3.0, 'Tex-Mex'],
 ['New Toronto', 3, 5.0, 'Cafes'],
 ['New Toronto', 37, 3.5, 'Sushi Bars'],
 ['New Toronto', 3, 5.0, 'Sandwiches'],
 ['New Toronto', 20, 3.5, 'Breakfast & Brunch'],
 ['New Toronto', 18, 4.0, 'Seafood'],
 ['New Toronto', 74, 2.5, 'Comfort Food'],
 ['New Toronto', 64, 3.0, 'Steakhouses'],
 ['New Toronto', 20, 3.5, 'Chinese'],
 ['New Toronto', 3, 4.5, 'Caribbean'],
 ['New Toronto', 2950],
 ['Long Branch', 186, 4.5, 'Pizza'],
 ['Long Branch', 210, 4.0, 'Burgers'],
 ['Long Branch', 72, 4.0, 'Seafood'],
 ['Long Branch', 117, 4.0, 'Vegan'],
 ['Long Branch', 81, 4.5, 'Indian'],
 ['Long Branch', 71, 4.0, 'Latin American'],
 ['Long Branch', 62, 4.5, 'Burmese'],
 ['Long Branch', 44, 4.5, 'Japanese'],
 ['Long Branch', 24, 5.0, 'Bistros'],
 ['Long Branch', 41, 4.0, 'Coffee Roasteries'],
 ['Long Branch', 74, 4.0, 'Pizza'],
 ['Long Branch', 35, 4.0, 'Mexican'],
 ['Long Branch', 34, 4.0, 'Thai'],
 ['Long Branch', 41, 4.0, 'Greek'],
 ['Long Branch', 116, 4.0, 'Japanese'],
 ['Long Branch', 36, 3.5, 'Italian'],
 ['Long Branch', 29, 4.5, 'Vietnamese'],
 ['Long Branch', 35, 4.0, 'Thai'],
 ['Long Branch', 61, 3.0, 'Bars'],
 ['Long Branch', 74, 4.0, 'Sandwiches'],
 ['Long Branch', 32, 4.0, 'Burgers'],
 ['Long Branch', 167, 3.5, 'Burgers'],
 ['Long Branch', 23, 4.0, 'Vietnamese'],
 ['Long Branch', 53, 4.0, 'Moroccan'],
 ['Long Branch', 8, 5.0, 'Korean'],
 ['Long Branch', 16, 4.5, 'Cafes'],
 ['Long Branch', 12, 4.5, 'Fast Food'],
 ['Long Branch', 18, 4.0, 'Fish & Chips'],
 ['Long Branch', 21, 3.5, 'Sushi Bars'],
 ['Long Branch', 9, 4.5, 'Burgers'],
 ['Long Branch', 118, 3.0, 'Barbeque'],
 ['Long Branch', 8, 4.5, 'Sandwiches'],
 ['Long Branch', 44, 3.5, 'Breakfast & Brunch'],
 ['Long Branch', 19, 3.5, 'Cafes'],
 ['Long Branch', 30, 3.5, 'Chinese'],
 ['Long Branch', 16, 3.5, 'Slovakian'],
 ['Long Branch', 12, 4.5, 'Coffee & Tea'],
 ['Long Branch', 49, 3.5, 'Salad'],
 ['Long Branch', 67, 3.0, 'Middle Eastern'],
 ['Long Branch', 17, 3.5, 'Hot Dogs'],
 ['Long Branch', 6, 4.5, 'Falafel'],
 ['Long Branch', 20, 3.5, 'Caribbean'],
 ['Long Branch', 3, 5.0, 'Mediterranean'],
 ['Long Branch', 8, 4.5, 'Fast Food'],
 ['Long Branch', 9, 3.5, 'Italian'],
 ['Long Branch', 13, 3.5, 'Sushi Bars'],
 ['Long Branch', 7, 4.0, 'Fast Food'],
 ['Long Branch', 46, 2.5, 'Breakfast & Brunch'],
 ['Long Branch', 3, 4.5, 'Cafes'],
 ['Long Branch', 9, 2.5, 'Canadian (New)'],
 ['Long Branch', 2306],
 ['Alderwood', 81, 4.5, 'Indian'],
 ['Alderwood', 186, 4.5, 'Pizza'],
 ['Alderwood', 207, 4.5, 'Breakfast & Brunch'],
 ['Alderwood', 62, 4.5, 'Burmese'],
 ['Alderwood', 210, 4.0, 'Burgers'],
 ['Alderwood', 74, 4.0, 'Pizza'],
 ['Alderwood', 24, 5.0, 'Bistros'],
 ['Alderwood', 72, 4.0, 'Seafood'],
 ['Alderwood', 117, 4.0, 'Vegan'],
 ['Alderwood', 71, 4.0, 'Latin American'],
 ['Alderwood', 147, 3.5, 'Canadian (New)'],
 ['Alderwood', 84, 4.0, 'Cocktail Bars'],
 ['Alderwood', 44, 4.5, 'Japanese'],
 ['Alderwood', 32, 4.0, 'Burgers'],
 ['Alderwood', 74, 4.0, 'Sandwiches'],
 ['Alderwood', 53, 4.0, 'Moroccan'],
 ['Alderwood', 167, 3.5, 'Burgers'],
 ['Alderwood', 41, 4.0, 'Coffee Roasteries'],
 ['Alderwood', 67, 4.0, 'Italian'],
 ['Alderwood', 69, 3.5, 'American (Traditional)'],
 ['Alderwood', 55, 4.0, 'Steakhouses'],
 ['Alderwood', 71, 3.5, 'Chicken Shop'],
 ['Alderwood', 35, 4.0, 'Mexican'],
 ['Alderwood', 23, 4.0, 'Vietnamese'],
 ['Alderwood', 34, 4.0, 'Thai'],
 ['Alderwood', 41, 4.0, 'Greek'],
 ['Alderwood', 64, 4.0, 'Seafood'],
 ['Alderwood', 15, 5.0, 'Sandwiches'],
 ['Alderwood', 29, 4.5, 'Vietnamese'],
 ['Alderwood', 118, 3.0, 'Barbeque'],
 ['Alderwood', 20, 4.5, 'Afghan'],
 ['Alderwood', 16, 3.5, 'Slovakian'],
 ['Alderwood', 36, 3.5, 'Italian'],
 ['Alderwood', 61, 3.0, 'Bars'],
 ['Alderwood', 110, 3.5, 'Bars'],
 ['Alderwood', 49, 3.5, 'Salad'],
 ['Alderwood', 12, 4.5, 'Coffee & Tea'],
 ['Alderwood', 64, 3.0, 'Canadian (New)'],
 ['Alderwood', 39, 3.5, 'Canadian (New)'],
 ['Alderwood', 49, 3.5, 'Seafood'],
 ['Alderwood', 67, 3.0, 'Middle Eastern'],
 ['Alderwood', 86, 2.5, 'Canadian (New)'],
 ['Alderwood', 22, 4.0, 'Pizza'],
 ['Alderwood', 57, 3.0, 'Burgers'],
 ['Alderwood', 21, 3.5, 'Sushi Bars'],
 ['Alderwood', 58, 3.0, 'Portuguese'],
 ['Alderwood', 9, 4.5, 'Burgers'],
 ['Alderwood', 8, 4.5, 'Sandwiches'],
 ['Alderwood', 38, 3.0, 'Wine Bars'],
 ['Alderwood', 8, 4.5, 'Fast Food'],
 ['Alderwood', 3197],
 ['Humber Summit', 75, 4.0, 'Italian'],
 ['Humber Summit', 66, 4.0, 'Pakistani'],
 ['Humber Summit', 146, 3.5, 'Burgers'],
 ['Humber Summit', 123, 3.5, 'Steakhouses'],
 ['Humber Summit', 6, 4.5, 'Caribbean'],
 ['Humber Summit', 8, 5.0, 'Italian'],
 ['Humber Summit', 17, 4.5, 'Bakeries'],
 ['Humber Summit', 63, 2.5, 'Mexican'],
 ['Humber Summit', 28, 4.0, 'Seafood'],
 ['Humber Summit', 29, 3.5, 'Italian'],
 ['Humber Summit', 29, 4.0, 'Breakfast & Brunch'],
 ['Humber Summit', 31, 3.5, 'Indian'],
 ['Humber Summit', 66, 3.0, 'Sushi Bars'],
 ['Humber Summit', 15, 4.5, 'Vietnamese'],
 ['Humber Summit', 35, 3.5, 'Indian'],
 ['Humber Summit', 22, 4.0, 'Chinese'],
 ['Humber Summit', 3, 5.0, 'Caribbean'],
 ['Humber Summit', 33, 3.5, 'Diners'],
 ['Humber Summit', 34, 3.5, 'Tex-Mex'],
 ['Humber Summit', 20, 4.0, 'Caribbean'],
 ['Humber Summit', 11, 4.0, 'Indian'],
 ['Humber Summit', 8, 4.5, 'Cafes'],
 ['Humber Summit', 6, 4.0, 'Lebanese'],
 ['Humber Summit', 33, 3.0, 'Indian'],
 ['Humber Summit', 14, 3.0, 'Hookah Bars'],
 ['Humber Summit', 2, 4.5, 'Colombian'],
 ['Humber Summit', 2, 4.5, 'Venues & Event Spaces'],
 ['Humber Summit', 2, 5.0, 'Burgers'],
 ['Humber Summit', 2, 5.0, 'Italian'],
 ['Humber Summit', 11, 3.0, 'Donairs'],
 ['Humber Summit', 4, 4.5, 'Vietnamese'],
 ['Humber Summit', 4, 4.5, 'Sandwiches'],
 ['Humber Summit', 10, 3.5, 'Chinese'],
 ['Humber Summit', 2, 5.0, 'Chinese'],
 ['Humber Summit', 4, 4.5, 'Portuguese'],
 ['Humber Summit', 1, 5.0, 'Breakfast & Brunch'],
 ['Humber Summit', 11, 3.5, 'Chinese'],
 ['Humber Summit', 4, 4.0, 'Italian'],
 ['Humber Summit', 6, 4.0, 'Caribbean'],
 ['Humber Summit', 10, 2.5, 'Hookah Bars'],
 ['Humber Summit', 1, 4.0, 'Pizza'],
 ['Humber Summit', 5, 3.0, 'Bakeries'],
 ['Humber Summit', 8, 3.0, 'Indian'],
 ...]

#add the hood rating to a dataframe
df_restaurants =pd.DataFrame(hood_ratings,columns=['neighborhood','review_count', 'rating', 'specialization'])
df_restaurants.head()

	neighborhood	review_count	rating	specialization
0	West Humber-Clairville	74	4.0	Indian
1	West Humber-Clairville	34	3.5	Indian
2	West Humber-Clairville	7	4.5	Middle Eastern
3	West Humber-Clairville	30	3.5	Caribbean
4	West Humber-Clairville	7	4.5	Burgers

#how many rows do we have
df_restaurants.shape

(6587, 4)

#We merged our existing dataframe with the Toronto dataset with this new restaurants dataset
df = pd.merge(df_restaurants,
                 df,
                 left_on='neighborhood', 
                 right_on='Neighbourhood')
df.head()

	neighborhood	review_count	rating	specialization	Neighbourhood	Population	Area	Income	Latitude	Longitude
0	West Humber-Clairville	74	4.0	Indian	West Humber-Clairville	33312.0	30.09	59703.0	43.735649	-79.625844
1	West Humber-Clairville	34	3.5	Indian	West Humber-Clairville	33312.0	30.09	59703.0	43.735649	-79.625844
2	West Humber-Clairville	7	4.5	Middle Eastern	West Humber-Clairville	33312.0	30.09	59703.0	43.735649	-79.625844
3	West Humber-Clairville	30	3.5	Caribbean	West Humber-Clairville	33312.0	30.09	59703.0	43.735649	-79.625844
4	West Humber-Clairville	7	4.5	Burgers	West Humber-Clairville	33312.0	30.09	59703.0	43.735649	-79.625844

# Drop unnecessary columns
df = df.drop(['Latitude', 'Longitude', 'neighborhood'], 1)
df.head()

	review_count	rating	specialization	Neighbourhood	Population	Area	Income
0	74	4.0	Indian	West Humber-Clairville	33312.0	30.09	59703.0
1	34	3.5	Indian	West Humber-Clairville	33312.0	30.09	59703.0
2	7	4.5	Middle Eastern	West Humber-Clairville	33312.0	30.09	59703.0
3	30	3.5	Caribbean	West Humber-Clairville	33312.0	30.09	59703.0
4	7	4.5	Burgers	West Humber-Clairville	33312.0	30.09	59703.0

I added a new feature called population density which basically show the number of people living in a square kilometer per neighborhood. Some neighborhoods will be more populated than others which might result in more restaurants and more people eating out

df['population_density'] = df['Population']/df['Area']
df.head()

	review_count	rating	specialization	Neighbourhood	Population	Area	Income	population_density
0	74	4.0	Indian	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764
1	34	3.5	Indian	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764
2	7	4.5	Middle Eastern	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764
3	30	3.5	Caribbean	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764
4	7	4.5	Burgers	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764

We are going to add a new column which categorises Income in three levels; low, medium and high income levels. We will use this to help in classification

According to the 2016 Canada census, the median household income in Toronto is C\$65,829. Low income households have an average income of C\\$32,000 per year, middle income households have an average income of C\$49,000 per year and high income households have an average income of C\$102,000 per year. We will use these income levels as a guide to classify the neighborhoods into three categories; low, medium and high income

df['income-group'] = df.loc[df['Income'] <= 43000, 'income-group'] = 'Low'
df['income-group'] = df.loc[df['Income'] > 43000, 'income-group'] = 'Medium'
df.head()

	review_count	rating	specialization	Neighbourhood	Population	Area	Income	population_density	income-group
0	74	4.0	Indian	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764	Medium
1	34	3.5	Indian	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764	Medium
2	7	4.5	Middle Eastern	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764	Medium
3	30	3.5	Caribbean	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764	Medium
4	7	4.5	Burgers	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764	Medium

Methodology

We want to know if there is a relationship between income and ratings therefore we will use Linear regression to see whether we can detect a relationship between the two parameters We first import matplotlib so that we can create scatter plots

import matplotlib.pyplot as plt
%matplotlib inline
print("Imported")

Imported

Lets plot income vs ratings and see if there's any linear relationship

#plot income against ratings
plt.scatter(df.rating, df.Income,  color='blue')
plt.xlabel("Rating")
plt.ylabel("Income")
plt.show()

There doesn't seem to be a linear relationship between income and ratings. Lets try with other features

#Lets see if there is a linear relationship between population and rating
plt.scatter(df.rating, df.Population,  color='blue')
plt.xlabel("Rating")
plt.ylabel("Population")
plt.show()

#Lets see if there is a linear relationship between population density and rating
plt.scatter(df.rating, df.population_density,  color='blue')
plt.xlabel("Rating")
plt.ylabel("Population Density")
plt.show()

#Lets see if there is a linear relationship between review count and rating
plt.scatter(df.rating, df.review_count,  color='blue')
plt.xlabel("Rating")
plt.ylabel("review_count")
plt.show()

**Our initial analysis shows that there is no linear relationship between income and ratings. We can tentatively conclude that a restaurants ratings are not influenced by the income levels of the neighboorhood its in. However to prove this lets create a model and evaluate its accuracy to see if we can get some good scores

Modelling

#import the sklearn linear regression model
from sklearn import linear_model
from sklearn.metrics import r2_score

df.head()

	review_count	rating	specialization	Neighbourhood	Population	Area	Income	population_density	income-group
0	74	4.0	Indian	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764	Medium
1	34	3.5	Indian	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764	Medium
2	7	4.5	Middle Eastern	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764	Medium
3	30	3.5	Caribbean	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764	Medium
4	7	4.5	Burgers	West Humber-Clairville	33312.0	30.09	59703.0	1107.078764	Medium

#model the data
regr = linear_model.LinearRegression()
x = np.asanyarray(df[['Income']])
y = np.asanyarray(df[['rating']])
regr.fit (x, y)
# The coefficients
print ('Coefficients: ', regr.coef_)
print ('Intercept: ',regr.intercept_)

Coefficients:  [[-4.03561091e-06]]
Intercept:  [4.01281647]

Model Evaluation

Lets compare the actual vs predicted values to calculate the accuracy of the regression model.

y_hat= regr.predict(x)


# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % regr.score(x, y))
print("Mean absolute error: %.2f" % np.mean(np.absolute(y_hat - y)))
print("Residual sum of squares (MSE): %.2f" % np.mean((y_hat - y) ** 2))
print("R2-score: %.2f" % r2_score(y_hat , y) )

Variance score: 0.01
Mean absolute error: 0.50
Residual sum of squares (MSE): 0.43
R2-score: -111.75

The model has a high negative R-square. This shows that there is no relationship at all between neighborhood income and a restaurant ratings. Lets try adding other parameters and see if it will make a difference.

#model the data
regr = linear_model.LinearRegression()
x = np.asanyarray(df[['review_count','population_density', 'Income']])
y = np.asanyarray(df[['rating']])
regr.fit (x, y)
# The coefficients
print ('Coefficients: ', regr.coef_)
print ('Intercept: ',regr.intercept_)

Coefficients:  [[ 2.24380281e-04  1.72870416e-05 -1.79816501e-06]]
Intercept:  [3.75518664]

Model Evaluation

Lets compare the actual vs predicted values to calculate the accuracy of the regression model.

y_hat= regr.predict(x)


# Explained variance score: 1 is perfect prediction
print('Variance score: %.2f' % regr.score(x, y))
print("Mean absolute error: %.2f" % np.mean(np.absolute(y_hat - y)))
print("Residual sum of squares (MSE): %.2f" % np.mean((y_hat - y) ** 2))
print("R2-score: %.2f" % r2_score(y_hat , y) )

Variance score: 0.03
Mean absolute error: 0.49
Residual sum of squares (MSE): 0.42
R2-score: -27.97

The R2 score is much better but still negative. This shows that the population of a neighborhood and/or its income levels have no coorelation with a restaurants ratings

Conclusion

It is good to know that the income of a neighborhood has nothing to do with the ratings of its restaurants. High income neighborhoods can have poorly rated restaurants and low income neighborhood can have highly rated restaurants. Also other metrics like the population density have no effect at all on a restaurant.

It would be more effective to try and use sentiment analysis to calculate the rating of a restaurant. This would involve analysing the text of the reviews to see if we can use that to calculate the rating a user would give a restaurant.